Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms.

Yu Miao,Alan Hunter,Ioannis Georgilas

doi:10.3390/s21217004

Abstract

Occupancy mapping is widely used to generate volumetric 3D environment models from point clouds, informing a robotic platform which parts of the environment are free and which are not. The selection of the parameters that govern the point cloud generation algorithms and mapping algorithms affects the process and the quality of the final map. Although previous studies have been reported in the literature on optimising major parameter configurations, research in the process to identify optimal parameter sets to achieve best occupancy mapping performance remains limited. The current work aims to fill this gap with a two-step principled methodology that first identifies the most significant parameters by conducting Neighbourhood Component Analysis on all parameters and then optimise those using grid search with the area under the Receiver Operating Characteristic curve. This study is conducted on 20 data sets with specially designed targets, providing precise ground truths for evaluation purposes. The methodology is tested on OctoMap with point clouds created by applying StereoSGBM on the images from a stereo camera. A clear indication can be seen that mapping parameters are more important than point cloud generation parameters. Moreover, up to 15% improvement in mapping performance can be achieved over default parameters.

Highlights

In robotics, occupancy maps have a wide range of applications, including spatial representation of the real world [1], navigation [2], motion planning [3] and autonomous driving [4]
false discovery rate (FDR) is similar to the case true positive rate (TPR) with the weights of OctoMap parameters and point cloud parameters mostly being above 0.5 and under
The baseline area under the curve (AUC) generated by OctoMap default parameters in two environments is normally better when boxes are covered with Voronoi diagrams

Summary

Introduction

Occupancy maps have a wide range of applications, including spatial representation of the real world [1], navigation [2], motion planning [3] and autonomous driving [4]. Maps are commonly generated from point clouds with a variety of sensors such as LIDAR [5], RGB-D cameras [6] and stereo cameras [7]. One popular occupancy mapping algorithm is OctoMap generating occupancy maps from these point clouds. 3D Point Cloud Generation from a Stereo Camera. For the case of a stereo camera the StereoSGBM algorithm [13] in the OpenCV library can generate the disparity map of left and right images. The point cloud can be reconstructed by the stereo camera model from this disparity map. D n is the difference between maximum disparity and minimum disparity. It must be a number greater than 0 and divisible by 16

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Methods

Results

Discussion

Conclusion